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dc.contributor.authorGholamizoj, Soroosh 17:57:00 (GMT) 17:57:00 (GMT)
dc.description.abstractIn proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database, failure of the software, or sub-optimal search parameters. Thus, spectrum quality assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses. We propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy. Furthermore, we propose a statistical model to automatically detect the enzyme used for digestion in a proteomics experiment, by analyzing the distribution of amino acids in peptides de novo sequenced with a nonspecific enzyme setting. Results demonstrate that this algorithm can accurately identify correct enzymes.en
dc.publisherUniversity of Waterlooen
dc.titlePredicting the Spectrum Quality and Digestive Enzyme for Shotgun Proteomicsen
dc.typeMaster Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorMa, Bin
uws.contributor.affiliation1Faculty of Mathematicsen

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